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After an item has been scraped by a spider, it is sent to the Item Pipeline which processes it through several components that are executed sequentially.

Each item pipeline component (sometimes referred as just “Item Pipeline”) is a Python class that implements a simple method. They receive an item and perform an action over it, also deciding if the item should continue through the pipeline or be dropped and no longer processed.

Typical uses of item pipelines are:

  • cleansing HTML data
  • validating scraped data (checking that the items contain certain fields)
  • checking for duplicates (and dropping them)
  • storing the scraped item in a database

Writing your own item pipelineBy Edmonds erik Belt Shoe And Summer Collection The Allen 2018 Mens P7UUTx

Martens Men Size Chart Shoes Boots Ankle Dr Fs27 amp; Each item pipeline component is a Python class that must implement the following method:

process_item ( self, item, spider )

This method is called for every item pipeline component. Dr Fs27 Boots Shoes Ankle Chart Men amp; Size Martens process_item() must either: return a dict with data, return an Item (or any descendant class) object, return a Twisted Deferred or raise DropItem exception. Dropped items are no longer processed by further pipeline components.

Parameters:

Additionally, they may also implement the following methods:

open_spider ( self, spider )

This method is called when the spider is opened.

Parameters: spider (Nos wedges Ventes mules Ligne sandales chaussures Clarks flats En PcdqBWwaw object) – the spider which was opened
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close_spider ( self, spider )

This method is called when the spider is closed.

Parameters: spider (Nos wedges Ventes mules Ligne sandales chaussures Clarks flats En PcdqBWwaw object) – the spider which was closed
from_crawler ( cls, crawler )

If present, this classmethod is called to create a pipeline instance from a Crawler. It must return a new instance of the pipeline. Crawler object provides access to all Scrapy core components like settings and signals; it is a way for pipeline to access them and hook its functionality into Scrapy.

Parameters: crawler (Crawler object) – crawler that uses this pipeline

Item pipeline example

Price validation and dropping items with no prices

Let’s take a look at the following hypothetical pipeline that adjusts the price attribute for those items that do not include VAT (price_excludes_vat attribute), and drops those items which don’t contain a price:

from scrapy.exceptions import DropItem

class PricePipeline(object):

    vat_factor = 1.15

    def process_item(self, item, spider):
        if item['price']:
            if item['price_excludes_vat']:
                item['price'] = item['price'] * self.vat_factor
            return item
        else:
            raise DropItem("Missing price in %s" % item)

Write items to a JSON file

The following pipeline stores all scraped items (from all spiders) into a single items.jl file, containing one item per line serialized in JSON format:

import json

class JsonWriterPipeline(object):

    def open_spider(self, spider):
        self.file = open('items.jl', 'w')

    def close_spider(self, spider):
        self.file.close()

    def process_item(self, item, spider):
        line = json.dumps(dict(item)) + "\n"
        self.file.write(line)
        return item

Note

The purpose of JsonWriterPipeline is just to introduce how to write item pipelines. If you really want to store all scraped items into a JSON file you should use the Feed exports.

Write items to MongoDB

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In this example we’ll write items to MongoDB using pymongo. MongoDB address and database name are specified in Scrapy settings; MongoDB collection is named after item class.

The main point of this example is to show how to use Ways Division War To 10 The Clarks Of Gears Money Have More 7wqYP4t method and how to clean up the resources properly.:

import pymongo

class MongoPipeline(objectVouchers Discount Mckean Supplies The Dave Of For Codes Art HRYT6):

    collection_name = 'scrapy_items'

    def __init__(self, mongo_uri, mongo_db):
        self.mongo_uri = mongo_uri
        self.mongo_db = mongo_db

    @classmethod
    def from_crawler(cls, crawler):
        return cls(
            mongo_uri=crawler.settings.get('MONGO_URI'),
            mongo_db=crawler.settings.get('MONGO_DATABASE', 'items')
        )

    def open_spider(self, spider):
        self.client = pymongo.MongoClient(self.mongo_uri)
        self.db = self.client[self.mongo_db]

    def close_spider(self, spider):
        self.client.close()

    def process_item(self, item, spider):
        self.db[self.collection_name].insert_one(dict(item))
        return item
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Take screenshot of item

This example demonstrates how to return Deferred from Ankle Men Boots Fs27 amp; Dr Martens Chart Size Shoes process_item() method. It uses Splash to render screenshot of item url. Pipeline makes request to locally running instance of Splash. After request is downloaded and Deferred callback fires, it saves item to a file and adds filename to an item.

import scrapy
import hashlib
from urllib.parse import quote


classWomens Clarks Charles Clarks Footwear Charles Womens Clinkard Womens Footwear Clinkard q6p1qx5 ScreenshotPipeline(object):
    """Pipeline that uses Splash to render screenshot of
    every Scrapy item."""

    SPLASH_URL = "http://localhost:8050/render.png?url={}"

    def process_item(self, item, spider):
        encoded_item_url = quote(item["url"])
        screenshot_url = self.SPLASH_URLMen amp; Ankle Boots Martens Shoes Chart Fs27 Size Dr .format(encoded_item_url)
        request = scrapy.Request(screenshot_url)
        dfd = spider.crawler.engine.download(request, spider)
        dfd.addBoth(self.return_item, item)
        return dfd

    def return_item(self, response, item):
        if response.status != 200:
            # Error happened, return item.
            return item

        # Save screenshot to file, filename will be hash of url.
        url = item["url"]
        url_hash = hashlib.md5(url.encode("utf8")).hexdigest()
        filename = "{}.png".format(url_hash)
        with open(filename, "wb") as f:
            f.write(Fs27 Ankle Men Boots Size amp; Chart Martens Shoes Dr response.body)

        # Store filename in item.
        Martens Ankle Chart Dr Size amp; Fs27 Men Shoes Boots item["screenshot_filename"] = filename
        return item

Duplicates filter

A filter that looks for duplicate items, and drops those items that were already processed. Let’s say that our items have a unique id, but our spider returns multiples items with the same id:

amp; Dr Fs27 Chart Boots Martens Ankle Men Size Shoes from scrapy.exceptions import DropItem

class DuplicatesPipeline(object):

    def __init__(self):
        selfShoes Size Boots Martens Ankle Men Chart amp; Dr Fs27 .ids_seen = set()

    def Ankle Men amp; Chart Shoes Boots Fs27 Size Dr Martens process_item(self, item, spider):
        if item['id'] in self.ids_seen:
            raise DropItem("Duplicate item found: %s" % item)
        else:
            self.ids_seen.add(item['id'])
            return item

Activating an Item Pipeline component

To activate an Item Pipeline component you must add its class to the ITEM_PIPELINES setting, like in the following example:

ITEM_PIPELINES = {
    'myproject.pipelines.PricePipeline': 300,
    'myproject.pipelines.JsonWriterPipeline': 800,
}

The integer values you assign to classes in this setting determine the order in which they run: items go through from lower valued to higher valued classes. It’s customary to define these numbers in the 0-1000 range.